Projects
Delay Discounting as a Latent Factor
Date: December 15, 2024
This project examines the latent factor structure of delay discounting, the tendency to prioritize immediate rewards over delayed ones, which is linked to impulsive behaviors such as substance abuse and poor academic performance. Previous studies have used various methods to measure delay discounting, but the findings have been inconsistent, partly due to differences in operationalization. This study uses confirmatory factor analysis (CFA) to explore the underlying latent factors of delay discounting and their relationship to behavioral outcomes, providing a clearer understanding of the construct and its implications.
One Factor Model:
CFI = .72
RMSEA = .24
SRMR = .109
Avg R2 = .60
Two Factor Model:
CFI = .94
RMSEA = .12
SRMR = .04
Avg R2 = .69
Four Factor Model:
CFI = .96
RMSEA = .10
SRMR = .04
Avg R2 = .69
Which ML Algorithms Predict Job Satisfaction The Best?
Date: May 2, 2023
Machine learning algorithms have gained significant popularity in I/O psychology due to their advanced learning capabilities, often outperforming traditional regression methods in predictive tasks. However, their “black-box” nature remains a challenge for research justification. This project compares the performance of baseline model logistic regression with popular algorithms KNN, and random forest in a 4-class job satisfaction classification task using the IBM HR dataset from Kaggle, comprising approximately 23,000 observations. Using lasso-based feature-selection methods, hyperparameter tuning, the project optimizes model performance and identifies the algorithm with the highest predictive accuracy. The findings offer actionable insights into employee well-being, showcasing the potential of data-driven approaches to enhance workforce engagement and organizational performance.